Literature DB >> 19393101

Simulation-based model checking approach to cell fate specification during Caenorhabditis elegans vulval development by hybrid functional Petri net with extension.

Chen Li1, Masao Nagasaki, Kazuko Ueno, Satoru Miyano.   

Abstract

BACKGROUND: Model checking approaches were applied to biological pathway validations around 2003. Recently, Fisher et al. have proved the importance of model checking approach by inferring new regulation of signaling crosstalk in C. elegans and confirming the regulation with biological experiments. They took a discrete and state-based approach to explore all possible states of the system underlying vulval precursor cell (VPC) fate specification for desired properties. However, since both discrete and continuous features appear to be an indispensable part of biological processes, it is more appropriate to use quantitative models to capture the dynamics of biological systems. Our key motivation of this paper is to establish a quantitative methodology to model and analyze in silico models incorporating the use of model checking approach.
RESULTS: A novel method of modeling and simulating biological systems with the use of model checking approach is proposed based on hybrid functional Petri net with extension (HFPNe) as the framework dealing with both discrete and continuous events. Firstly, we construct a quantitative VPC fate model with 1761 components by using HFPNe. Secondly, we employ two major biological fate determination rules - Rule I and Rule II - to VPC fate model. We then conduct 10,000 simulations for each of 48 sets of different genotypes, investigate variations of cell fate patterns under each genotype, and validate the two rules by comparing three simulation targets consisting of fate patterns obtained from in silico and in vivo experiments. In particular, an evaluation was successfully done by using our VPC fate model to investigate one target derived from biological experiments involving hybrid lineage observations. However, the understandings of hybrid lineages are hard to make on a discrete model because the hybrid lineage occurs when the system comes close to certain thresholds as discussed by Sternberg and Horvitz in 1986. Our simulation results suggest that: Rule I that cannot be applied with qualitative based model checking, is more reasonable than Rule II owing to the high coverage of predicted fate patterns (except for the genotype of lin-15ko; lin-12ko double mutants). More insights are also suggested.
CONCLUSION: The quantitative simulation-based model checking approach is a useful means to provide us valuable biological insights and better understandings of biological systems and observation data that may be hard to capture with the qualitative one.

Entities:  

Mesh:

Year:  2009        PMID: 19393101      PMCID: PMC2691733          DOI: 10.1186/1752-0509-3-42

Source DB:  PubMed          Journal:  BMC Syst Biol        ISSN: 1752-0509


  35 in total

1.  Constructing biological pathway models with hybrid functional Petri nets.

Authors:  Atsushi Doi; Sachie Fujita; Hiroshi Matsuno; Masao Nagasaki; Satoru Miyano
Journal:  In Silico Biol       Date:  2004

2.  A versatile petri net based architecture for modeling and simulation of complex biological processes.

Authors:  Masao Nagasaki; Atsushi Doi; Hiroshi Matsuno; Satoru Miyano
Journal:  Genome Inform       Date:  2004

3.  Validation of qualitative models of genetic regulatory networks by model checking: analysis of the nutritional stress response in Escherichia coli.

Authors:  Grégory Batt; Delphine Ropers; Hidde de Jong; Johannes Geiselmann; Radu Mateescu; Michel Page; Dominique Schneider
Journal:  Bioinformatics       Date:  2005-06       Impact factor: 6.937

4.  The combined action of two intercellular signaling pathways specifies three cell fates during vulval induction in C. elegans.

Authors:  P W Sternberg; H R Horvitz
Journal:  Cell       Date:  1989-08-25       Impact factor: 41.582

5.  MAP kinase signaling specificity mediated by the LIN-1 Ets/LIN-31 WH transcription factor complex during C. elegans vulval induction.

Authors:  P B Tan; M R Lackner; S K Kim
Journal:  Cell       Date:  1998-05-15       Impact factor: 41.582

6.  A genetic pathway for the specification of the vulval cell lineages of Caenorhabditis elegans.

Authors:  E L Ferguson; P W Sternberg; H R Horvitz
Journal:  Nature       Date:  1987 Mar 19-25       Impact factor: 49.962

7.  lin-31, a Caenorhabditis elegans HNF-3/fork head transcription factor homolog, specifies three alternative cell fates in vulval development.

Authors:  L M Miller; M E Gallegos; B A Morisseau; S K Kim
Journal:  Genes Dev       Date:  1993-06       Impact factor: 11.361

8.  The C. elegans homolog of the mammalian tumor suppressor Dep-1/Scc1 inhibits EGFR signaling to regulate binary cell fate decisions.

Authors:  Thomas A Berset; Erika Fröhli Hoier; Alex Hajnal
Journal:  Genes Dev       Date:  2005-05-18       Impact factor: 11.361

9.  A MAP kinase homolog, mpk-1, is involved in ras-mediated induction of vulval cell fates in Caenorhabditis elegans.

Authors:  M R Lackner; K Kornfeld; L M Miller; H R Horvitz; S K Kim
Journal:  Genes Dev       Date:  1994-01       Impact factor: 11.361

10.  lag-1, a gene required for lin-12 and glp-1 signaling in Caenorhabditis elegans, is homologous to human CBF1 and Drosophila Su(H).

Authors:  S Christensen; V Kodoyianni; M Bosenberg; L Friedman; J Kimble
Journal:  Development       Date:  1996-05       Impact factor: 6.868

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  14 in total

1.  Quantitative variation in autocrine signaling and pathway crosstalk in the Caenorhabditis vulval network.

Authors:  Erika Hoyos; Kerry Kim; Josselin Milloz; Michalis Barkoulas; Jean-Baptiste Pénigault; Edwin Munro; Marie-Anne Félix
Journal:  Curr Biol       Date:  2011-03-31       Impact factor: 10.834

2.  Simulation of a Petri net-based model of the terpenoid biosynthesis pathway.

Authors:  Aliah Hazmah Hawari; Zeti-Azura Mohamed-Hussein
Journal:  BMC Bioinformatics       Date:  2010-02-09       Impact factor: 3.169

3.  Time-dependent structural transformation analysis to high-level Petri net model with active state transition diagram.

Authors:  Chen Li; Masao Nagasaki; Ayumu Saito; Satoru Miyano
Journal:  BMC Syst Biol       Date:  2010-04-01

4.  Logic programming to predict cell fate patterns and retrodict genotypes in organogenesis.

Authors:  Benjamin A Hall; Ethan Jackson; Alex Hajnal; Jasmin Fisher
Journal:  J R Soc Interface       Date:  2014-09-06       Impact factor: 4.118

5.  "Antelope": a hybrid-logic model checker for branching-time Boolean GRN analysis.

Authors:  Gustavo Arellano; Julián Argil; Eugenio Azpeitia; Mariana Benítez; Miguel Carrillo; Pedro Góngora; David A Rosenblueth; Elena R Alvarez-Buylla
Journal:  BMC Bioinformatics       Date:  2011-12-22       Impact factor: 3.307

6.  A checkpoints capturing timing-robust Boolean model of the budding yeast cell cycle regulatory network.

Authors:  Changki Hong; Minho Lee; Dongsup Kim; Dongsan Kim; Kwang-Hyun Cho; Insik Shin
Journal:  BMC Syst Biol       Date:  2012-09-28

7.  A Boolean probabilistic model of metabolic adaptation to oxygen in relation to iron homeostasis and oxidative stress.

Authors:  Fiona Achcar; Jean-Michel Camadro; Denis Mestivier
Journal:  BMC Syst Biol       Date:  2011-04-13

8.  A hybrid model of mammalian cell cycle regulation.

Authors:  Rajat Singhania; R Michael Sramkoski; James W Jacobberger; John J Tyson
Journal:  PLoS Comput Biol       Date:  2011-02-10       Impact factor: 4.475

9.  Structural analysis to determine the core of hypoxia response network.

Authors:  Monika Heiner; K Sriram
Journal:  PLoS One       Date:  2010-01-19       Impact factor: 3.240

10.  A network model for the specification of vulval precursor cells and cell fusion control in Caenorhabditis elegans.

Authors:  Nathan Weinstein; Luis Mendoza
Journal:  Front Genet       Date:  2013-06-14       Impact factor: 4.599

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